zoukankan      html  css  js  c++  java
  • 5.非线性回归

    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    # numpy生成200个随机点
    x_data = np.linspace(-0.5,0.5,200)[:,np.newaxis]
    noise = np.random.normal(0,0.02,x_data.shape)
    y_data = np.square(x_data) + noise
    
    plt.scatter(x_data, y_data)
    plt.show()

    # 定义两个placeholder
    x = tf.placeholder(tf.float32,[None,1])
    y = tf.placeholder(tf.float32,[None,1])
    
    # 神经网络结构:1-30-1
    w1 = tf.Variable(tf.random_normal([1,30]))
    b1 = tf.Variable(tf.zeros([30]))
    wx_plus_b_1 = tf.matmul(x,w1) + b1
    l1 = tf.nn.tanh(wx_plus_b_1)
    
    w2 = tf.Variable(tf.random_normal([30,1]))
    b2 = tf.Variable(tf.zeros([1]))
    wx_plus_b_2 = tf.matmul(l1,w2) + b2
    prediction = tf.nn.tanh(wx_plus_b_2)
    
    # 二次代价函数
    loss = tf.losses.mean_squared_error(y,prediction)
    # 使用梯度下降法最小化loss
    train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    
    with tf.Session() as sess:
        # 变量初始化
        sess.run(tf.global_variables_initializer())
        for _ in range(3000):
            sess.run(train,feed_dict={x:x_data,y:y_data})
            
        # 获得预测值
        prediction_value = sess.run(prediction,feed_dict={x:x_data})
        # 画图
        plt.scatter(x_data, y_data)
        plt.plot(x_data, prediction_value, 'r-', lw=5)
        plt.show()

  • 相关阅读:
    jQuery之元素操作及事件绑定
    JS中常遇到的浏览器兼容问题和解决方法
    九九乘法表
    全选复习
    css基本知识
    js数组
    Spark常见错误问题汇总
    被问懵逼的Kafka面试题
    被问懵逼的数仓面试
    Flink模拟项目: 订单支付实时监控
  • 原文地址:https://www.cnblogs.com/liuwenhua/p/11605364.html
Copyright © 2011-2022 走看看